Package: beyondWhittle 1.3.0

Renate Meyer

beyondWhittle: Bayesian Spectral Inference for Time Series

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Authors:Alexander Meier [aut], Claudia Kirch [aut], Matthew C. Edwards [aut], Renate Meyer [aut, cre], Yifu Tang [aut]

beyondWhittle_1.3.0.tar.gz
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beyondWhittle.pdf |beyondWhittle.html✨
beyondWhittle/json (API)

# Install 'beyondWhittle' in R:
install.packages('beyondWhittle', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.30 score 2 stars 424 downloads 21 exports 47 dependencies

Last updated 4 months agofrom:bbdf52ae70. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
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R-4.5-linux-x86_64OKMar 25 2025
R-4.4-linux-x86_64OKMar 25 2025

Exports:bayes_factorbdp_dw_bayes_factor_k1bdp_dw_est_post_statsbdp_dw_mcmc_params_genbdp_dw_prior_params_genfourier_freqgibbs_argibbs_bdp_dwgibbs_npgibbs_npcgibbs_vargibbs_vnplocal_moving_FT_zigzagpacf_to_arpsd_armapsd_tvarma12psd_varmarmvnormscree_type_arsim_tvarma12sim_varma

Dependencies:BHclicolorspacecurlfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestltsamagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgconfigquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo

Citation

To cite package ‘beyondWhittle’ in publications use:

Meier A, Kirch C, Edwards M, Meyer R, Tang Y (2024). beyondWhittle: Bayesian Spectral Inference for Time Series. R package version 1.3.0, https://CRAN.R-project.org/package=beyondWhittle.

Corresponding BibTeX entry: 

  @Manual{,
    title = {beyondWhittle: Bayesian Spectral Inference for Time
      Series},
    author = {Alexander Meier and Claudia Kirch and Matthew C. Edwards
      and Renate Meyer and Yifu Tang},
    year = {2024},
    note = {R package version 1.3.0},
    url = {https://CRAN.R-project.org/package=beyondWhittle},
  }

Readme and manuals

Help Manual

Help pageTopics
Bayesian spectral inference for time seriesbeyondWhittle-package beyondWhittle
a generic method for bdp_dw_result classbayes_factor
Extracting the Bayes factor of k1=1 from bdp_dw_result classbayes_factor.bdp_dw_result
Estimating the Bayes factor of hypothesis "k1 = 1".bdp_dw_bayes_factor_k1
Calculating the estimated posterior mean, median and credible region (tv-PSD)bdp_dw_est_post_stats
Generate a list of values for MCMC algorithmbdp_dw_mcmc_params_gen
Generate a list of parameter values in prior elicitationbdp_dw_prior_params_gen
Fourier frequenciesfourier_freq
Gibbs sampler for an autoregressive model with PACF parametrization.gibbs_ar
BDP-DW method: performing posterior sampling and calculating statistics based on the posterior samplesgibbs_bdp_dw
Gibbs sampler for Bayesian nonparametric inference with Whittle likelihoodgibbs_np
Gibbs sampler for Bayesian semiparametric inference with the corrected AR likelihoodgibbs_npc
Gibbs sampler for vector autoregressive model.gibbs_var
Gibbs sampler for multivaiate Bayesian nonparametric inference with Whittle likelihoodgibbs_vnp
Calculate the moving Fourier transform ordinateslocal_moving_FT_zigzag
Convert partial autocorrelation coefficients to AR coefficients.pacf_to_ar
Plot method for bdp_dw_result classplot.bdp_dw_result
Plot method for bdp_dw_tv_psd classplot.bdp_dw_tv_psd
Plot method for gibbs_psd classplot.gibbs_psd
Print method for bdp_dw_result classprint.bdp_dw_result
Print method for gibbs_psd classprint.gibbs_psd
ARMA(p,q) spectral density functionpsd_arma
time-varying spectral density function of the tvARMA(1,2) processes for illustrationspsd_tvarma12
VARMA(p,q) spectral density functionpsd_varma
Simulate from a Multivariate Normal Distributionrmvnorm
Negative log AR likelihood values for scree-type plotsscree_type_ar
simulate from the tvARMA(1,2) process for illustrationsim_tvarma12
Simulate from a VARMA modelsim_varma
Summary method for bdp_dw_result classsummary.bdp_dw_result
Summary method for gibbs_psd classsummary.gibbs_psd